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YXBIAN/EarthquakeNet

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Use restrictions

The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-sa-4.0, from the tagEvidence: README.md at c53ad07
Licence files: LICENSE, detected as cc-by-nc-sa-4.0Evidence: LICENSE at c53ad07
Declared sources: None listed in the card metadata
Access gated on Hugging Face: Yes

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Tasks

image-segmentation

Citation text from the card

@INPROCEEDINGS{10648157,
  author={Jiang, Shenlu and Bian, Yuxin and Wang, Yiran and Li, Xufeng and Liu, Zhankeng and Ren, Yi and Zhao, Yunxuan},
  booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, 
  title={EarthquakeNet: A High-Resolution UAV-Based Dataset for Earthquake Damage Assessment}, 
  year={2024},
  volume={},
  number={},
  pages={55-61},
  abstract={Advancements in computer vision and deep learning have significantly propelled progress in scene understanding, aiding rescue teams in accurately assessing damage after natural disasters. In this paper, we introduce EarthquakeNet, a meticulously curated high-resolution post-earthquake dataset featuring detailed classification and semantic segmentation annotations, designed to enhance comprehensive scene understanding following natural disasters. EarthquakeNet comprises post-disaster images captured using unmanned aerial vehicles (UAVs) from multiple affected areas after an earthquake. The uniqueness of EarthquakeNet lies in providing high-resolution post-disaster imagery, each with exhaustive annotations. Unlike existing datasets that offer annotations for specific scene elements like buildings, EarthquakeNet provides pixel-level annotations for a broader range of categories, including roads, houses, and tents. We also demonstrate the utility of the dataset by implementing state-of-the-art segmentation models on EarthquakeNet, showcasing its value in improving existing methods for natural disa

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dataset_licence({ "id": "YXBIAN/EarthquakeNet" })
signal: "restrictive"
stated_licence: "cc-by-nc-sa-4.0"
sources: 0